Sasha Eizenberg

EHR and Imaging AI Integration: Decision Support Beyond Pixel Analysis

In the past few years, we’ve seen emerging technological capabilities come to light via integrations between native systems and AI platforms. These integrations further enrich the healthcare AI experience while paving the way for more informed clinical decisions to be made on the fly.

The Aidoc aiOS™ EHR integration completes the clinical snapshot, making it easier for the right information to reach the right physicians at the right time. It is capable of integrating with any EHR, and is available as part of Epic’s App Market with a lightweight implementation.

The above is an example of the labs and vitals screen for a suspected aortic dissection case.

How EHR and AI Integrations Augment the AI Experience

Smart(er) AI

Aidoc’s FHIR integration into EHRs means that patient history, clinical vitals and labs are automatically made part of the decision making process, providing clinical context beyond the pixel-level. AI-based triage is ultimately more accurate and relevant when enhanced with a patient’s clinical information.

Actionable AI

With key clinical information made easily accessible, your AI experience becomes actionable. The information you need is in one place, enabling prompt decisions with regard to interventions, OR prep and clinical decisions without needing to thumb through an abundance of irrelevant information and context-switching between interfaces and applications. EHR integration also facilitates patient management by tracking follow imaging recommendations over time, ensuring that no patients fall through the cracks in inundated health systems.

Measurable AI

In addition to improving existing workflows, an EHR AI integration allows for advanced research and value demonstration capabilities. EHR integration seamlessly creates a bridge to key clinical data for research. While leading institutions have already published over 80 clinical papers/abstracts on various topics on the Aidoc platform, access to structured clinical data enables you to measure even more complex inputs like patient outcomes, time to treatment and length of stay.    

Sasha Eizenberg